Carnegie Mellon University

Yiming Yang

Yiming Yang

Professor, Language Technologies Institute

  • 6717 —Gates & Hillman Centers
  • 412-268-1364


My research has centered on statistical learning methods/algorithms and application to very-large-scale text categorization, web-mining for concept graph discovery, semi-supervised clustering, multitask learning, novelty-based information retrieval, large-scale optimization for online advertising, social network analysis for personalized email prioritization, etc. My recent research focuses on the following topics:

Large-Scale Structured Learning for Hierarchical Classification  (Gopal & Yang, KDD 2013 Gopal & Yang, ICML 2013 & Supplementary  ;  Gopal et al., NIPS 2012)

  • Providing organizational views of multi-source Big Data (e.g., Wikipedia, online shops, Coursera)
  • State-of-the-art classifiers for large-scale classification over hundreds of thousands of categories
  • Scalable variational inference for joint optimization of one trillion (4 TB) model parameters

Scalable Machine Learning for Time Series Analysis (Topic Detection and Tracking)

Concept Graph Learning for Online Education (NSF project; Yang et al., WSDM 2015)

  • Mapping online course materials to Wikipedia categories as the Interlingua (universal concepts)
  • Predicting conceptual dependencies among courses based on partially observed prerequisites
  • Planning customized curriculum for individuals based on backgrounds and goals

Macro-Level Information Fusion for Events and Entities (joint effort with Jaime Carbonell in the DARPA DEFT project)

  • Detecting entities and events of interest in various forms of mentions in text to enable high-precision, semi-structured information fusion and summarization. Using a corporate acquisition event as an example, different (and partially redundant) sentences can mention acquirer, price, date, approvals, joint-management, etc. This multi-aspect information needs to be jointly extracted into a unified structured form for this event type, with uncertainty estimates in the aggregated representation.